Human pose, hand and mesh estimation using deep learning: a survey

Toshpulatov, Mukhiddin; Lee, Wookey; Lee, Suan; Roudsari, Arousha Haghighian

doi:10.1007/s11227-021-04184-7

Cited by 33 publications

(14 citation statements)

References 108 publications

(143 reference statements)

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“…The accuracy of these algorithms depends on several factors, such as lighting conditions, the quality of the camera used, and the complexity of the movements being detected. Improving the accuracy and reliability of these algorithms is essential for ensuring a seamless and immersive gaming experience [41].…”

Section: Discussionmentioning

confidence: 99%

Development of Computer Vision-enabled Augmented Reality Games to Increase Motivation for Sports

Doskarayev¹,

Omarov²,

Omarov³

et al. 2023

IJACSA

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This research paper presents the development of computer vision-enabled augmented reality games based on action detection to increase motivation for sports. With the increasing popularity of digital games, physical activity and sports participation have been declining, especially among the younger generation. To address this issue, we developed a series of augmented reality games that require players to perform physical actions to progress and succeed in the game. These games were developed using computer vision technology to detect the players' movements and provide real-time feedback, enhancing the gaming experience and promoting physical activity. The results of our user study showed that participants who played the augmented reality games reported higher levels of motivation to engage in physical activity and sports. The findings suggest that computer vision-enabled augmented reality games can be an effective tool to promote physical activity and sports participation, especially among younger generations.

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Section: Discussionmentioning

confidence: 99%

Development of Computer Vision-enabled Augmented Reality Games to Increase Motivation for Sports

Doskarayev¹,

Omarov²,

Omarov³

et al. 2023

IJACSA

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“…These techniques have been successfully applied to in-depth pose face recognition from images. The second group is multi-pose area approaches, which integrate the internal capabilities of face angles into a shared latent region that allows for the Both of these approaches are elaborated in [21], [22]. The authors in [23] proposed a pose estimation method based on bin classification.…”

Section: A Depth Pose Alignment Of Imagementioning

confidence: 99%

Deep Learning Based Multi Pose Human Face Matching System

Sohail,

Shoukat,

Khan

et al. 2024

IEEE Access

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Current techniques for multi-pose human face matching yield suboptimal outcomes because of the intricate nature of pose equalization and face rotation. Deep learning models, such as YOLO-V5, etc., that have been proposed to tackle these complexities, suffer from slow frame matching speeds and therefore exhibit low face recognition accuracy. Concerning this, certain literature investigated multi-pose human face detection systems; however, those studies are of elementary level and do not adequately analyze the utility of those systems. To fill this research gap, we propose a real-time face matching algorithm based on YOLO-V5. Our algorithm utilizes multi-pose human patterns and considers various face orientations, including organizational faces and left, right, top, and bottom alignments, to recognize multiple aspects of people. Using face poses, the algorithm identifies face positions in a dataset of images obtained from mixed pattern live streams, and compares faces with a specific piece of the face that has a relatively similar spectrum for matching with a given dataset. Once a match is found, the algorithm displays the face on Google Colab, collected during the learning phase with the Robo-flow key, and tracks it using the YOLO-V5 face monitor. Alignment variations are broken up into different positions, where each type of face is uniquely learned to have its own study demonstrated. This method offers several benefits for identifying and monitoring humans using their labeling tag as a pattern name, including high face-matching accuracy and minimum speed of owing face-to-pose variations. Furthermore, the algorithm addresses the face rotation issue by introducing a mixture of error functions for execution time, accuracy loss, frame-wise failure, and identity loss, attempting to guide the authenticity of the produced image frame. Experimental results confirm effectiveness of the algorithm in terms of improved accuracy and reduced delay in the face-matching paradigm.

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“…Computer-vision-based Human Pose Estimation (HPE) methods including conventional and instance-based pose estimation models to novel deep network architectures can detect human body poses in 2D or 3D space by regressing skeletal joint angles or critical points using a single view or several view cameras with monocular or depth modalities [52]- [57]. However, most of the pose estimation algorithms are designed for adults or pedestrians, and few solutions have focussed on special needs children or pediatric healthcare.…”

Section: B Human Pose Estimationmentioning

confidence: 99%

Computer Vision-Based Assessment of Autistic Children: Analyzing Interactions, Emotions, Human Pose, and Life Skills

Prakash¹,

Kohli²,

Kohli³

et al. 2023

IEEE Access

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In this paper, the proposed work tests the computer vision application to perform the skill and emotion assessment of children with Autism Spectrum Disorder (ASD) by extracting various bio-behaviors, human activities, child-therapist interactions, and joint pose estimations from the video-recorded interactive singleor two-person play-based intervention sessions. A comprehensive data set of 300 videos are amassed from ASD children engaged in social interaction and developed three novel deep learning-based computer vision models which are explained as follows: 1) activity comprehension to analyze child-play partner interactions (Activity Comprehension model); 2) an automatic joint attention recognition framework using pose, and 3) emotion and facial expression recognition. We tested models on children's real-world unseen 68 videos captured from the clinic and public datasets. The activity comprehension model has an overall accuracy of 72.32%, the joint attention models have an accuracy of 97% for following eye gaze and 93.4% for hand pointing and the facial expression recognition model has an overall accuracy of 95.1%. The proposed models could extract activities and behaviors of interest from free-play and intervention session videos, empowering clinicians with data useful in diagnosis, assessment, treatment formulation, and monitoring of ASD children with limited supervision.

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Human pose, hand and mesh estimation using deep learning: a survey

Cited by 33 publications

References 108 publications

Development of Computer Vision-enabled Augmented Reality Games to Increase Motivation for Sports

Development of Computer Vision-enabled Augmented Reality Games to Increase Motivation for Sports

Deep Learning Based Multi Pose Human Face Matching System

Computer Vision-Based Assessment of Autistic Children: Analyzing Interactions, Emotions, Human Pose, and Life Skills

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